Functional Programming

Monday, May 20

Today we will…

  • Project Proposal + Data
  • New Material1
    • Performing Repeated Tasks + Vectorization
    • Functional Programming: the map() family
  • PA 8: The Twelve Days of Christmas Starter Functions

Project Proposal + Data

You must complete the objectives and write up the written components outlined under Section 1 on the Project Details page on Canvas.

  • Due on Canvas by 11:59pm on Tuesday, 5/28 (Tuesday follows a Monday schedule…).

Performing Repeated Tasks

Repetition

Type out the task over and over.

https://bookdown.org/hneth/ds4psyl

Do not do this.

Iteration

Repeatedly execute the same operation over and over.

  • Loops (e.g., for() and while()) allow us to iterate.
for(i in 1:6){
  print(i^2)
}
[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36

https://bookdown.org/hneth/ds4psyl

Iteration

Repeatedly execute the same operation over and over.

  • Loops (e.g., for() and while()) allow us to iterate.
for(i in 1:6){
  print(i^2)
}
[1] 1
[1] 4
[1] 9
[1] 16
[1] 25
[1] 36
  • But loops tend to be slow!

https://bookdown.org/hneth/ds4psyl

Vectorization

Many operations in R are vectorized.

  • These functions operate on vectors of values rather than a single value.
  • We can iterate without writing a loop.
x <- seq(from = -4, to = 6)
x
 [1] -4 -3 -2 -1  0  1  2  3  4  5  6

Loop:

for(i in 1:length(x)){
  x[i] <- abs(x[i])
}
x
 [1] 4 3 2 1 0 1 2 3 4 5 6

Vectorization

Many operations in R are vectorized.

  • These functions operate on vectors of values rather than a single value.
  • We can iterate without writing a loop.
x <- seq(from = -4, to = 6)
x
 [1] -4 -3 -2 -1  0  1  2  3  4  5  6

Loop:

for(i in 1:length(x)){
  x[i] <- abs(x[i])
}
x
 [1] 4 3 2 1 0 1 2 3 4 5 6

Vectorized:

abs(x)
 [1] 4 3 2 1 0 1 2 3 4 5 6

Vectorization

Not every function is vectorized.

  • E.g., a function using if() statements cannot operate on vectors.
pos_neg_zero <- function(x){
  if(x > 0){
    return("Greater than 0!")
  } else if (x < 0){
    return("Less than 0!")
  } else {
    return("Equal to 0!")
  }
}

x <- seq(from = -4, to = 4)
pos_neg_zero(x)
Error in if (x > 0) {: the condition has length > 1

The if(x > 0) statement can only be checked for something of length 1 (a single number, not a vector).

Vectorization

Not every function is vectorized.

  • E.g., a function using if() statements cannot operate on vectors.
pos_neg_zero <- function(x){
  if(x > 0){
    return("Greater than 0!")
  } else if (x < 0){
    return("Less than 0!")
  } else {
    return("Equal to 0!")
  }
}

x <- seq(from = -4, to = 3)
pos_neg_zero(x)
Error in if (x > 0) {: the condition has length > 1
result <- rep(NA, length(x))
for(i in 1:length(x)){
  result[i] <- pos_neg_zero(x[i])
}

result
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"

Vectorization

Not every function is vectorized.

  • Vectorized versions of if() statements?

if_else() and case_when()

pos_neg_zero <- function(x){
  state <- case_when(x > 0 ~ "Greater than 0!", 
                     x < 0 ~ "Less than 0!", 
                     .default = "Equal to 0!")
  return(state)
}

x <- seq(from = -4, to = 3)
pos_neg_zero(x)
[1] "Less than 0!"    "Less than 0!"    "Less than 0!"    "Less than 0!"   
[5] "Equal to 0!"     "Greater than 0!" "Greater than 0!" "Greater than 0!"

Some functions cannot be vectorized!

Applying class() to a single variable in a dataframe returns the data type of that column:

class(penguins[[1]])
[1] "factor"

Trying to apply class() to every variable in a dataframe returns the data type of the dataframe:

class(penguins)
[1] "tbl_df"     "tbl"        "data.frame"

What can we do instead?

Write a for() loop…

data_type <- rep(NA, length = ncol(penguins))
for(i in 1:ncol(penguins)){
  data_type[i] <- class(penguins[[i]])
}

# format table nicely
tibble(column = names(penguins), 
       type = data_type) |> 
  pivot_wider(names_from = column, 
              values_from = type) |>  
  knitr::kable() |>
  kableExtra::kable_styling(font_size = 30)
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
factor factor numeric numeric integer integer factor integer

… but loops are computationally intensive!

What can we do instead?

What about across()?

  • Easily perform the same operation on multiple columns.
penguins |> 
  summarise(across(.cols = everything(), 
                   .fns = class))
# A tibble: 1 × 8
  species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <chr>   <chr>  <chr>          <chr>         <chr>             <chr>      
1 factor  factor numeric        numeric       integer           integer    
# ℹ 2 more variables: sex <chr>, year <chr>

Ugh. Internally, across() uses a for() loop!

for (j in seq_fns) {
  fn <- fns[[j]]
  out[[k]] <- fn(col, ...)
  k <- k + 1L

What can we do instead?


…

Functional Programming

To understand computations in R, two slogans are helpful:

  • Everything that exists is an object.

  • Everything that happens is a function call.

John Chambers (creator of the pre-cursor to R)

Functional Programming

What’s the big picture?

  • We will use functions as the arguments to other functions!


Note

There are a slew of apply() functions you will likely come across.

We will instead focus on the purrr package and the map() family of functions.

purrr

The purrr package breaks common list manipulations into small, independent pieces.

This strategy involves two steps:

  1. Break a complex problem into smaller sub-problems.
    • We then compose the small pieces together with |>.
  2. Solve each sub-problem for a single element of a list.
    • Once done, purrr will generalize the solution to every element in the list.

Brief Review: Lists

A list is a 1-dimensional, heterogeneous data structure.

  • There are no restrictions on what data type or structure it can contain – values, vectors, other lists, dataframes, etc.
  • Lists are indexed with [] or [[]].
[[1]]
[1]  TRUE FALSE  TRUE  TRUE

[[2]]
     [,1] [,2]
[1,] 6.70  4.4
[2,] 5.58  6.0

[[3]]
[1] "A"
my_list[1]
[[1]]
[1]  TRUE FALSE  TRUE  TRUE
my_list[[2]]
     [,1] [,2]
[1,] 6.70  4.4
[2,] 5.58  6.0
my_list[[2]][1,2]
[1] 4.4

Brief Review: Lists

A dataframe / tibble is a specially formatted list of columns!

small_penguins <- penguins[1:8,]
small_penguins[3]
# A tibble: 8 × 1
  bill_length_mm
           <dbl>
1           39.1
2           39.5
3           40.3
4           NA  
5           36.7
6           39.3
7           38.9
8           39.2
small_penguins[[3]]
[1] 39.1 39.5 40.3   NA 36.7 39.3 38.9 39.2

The purrr package works for lists, so it works for dataframes.

map()

The map() function iterates through each item in a list and applies a function, then returns the new list.

Note: the first argument in map() is the list, so if we pipe into it, we only specify the function!

map() + Dataframes

A dataframe is just a list of columns – map() will apply a function to every column.

penguins |> 
  select(bill_length_mm:body_mass_g) |>
  map(~ mean(.x, na.rm = TRUE))
$bill_length_mm
[1] 43.92193

$bill_depth_mm
[1] 17.15117

$flipper_length_mm
[1] 200.9152

$body_mass_g
[1] 4201.754

Use a lambda function (with ~ and .x), just like in across()!

The map() Family

The map_xxx() variants allow you to specify the type of output you want.

  • map() creates a list.
  • map_chr() creates a character vector.
  • map_lgl() creates an logical vector.
  • map_int() creates a integer vector.
  • map_dbl() creates a numeric vector.

All take in a list and a function as arguments.

map() + penguins

Calculate the mean of each column.

penguins |> 
  select(bill_length_mm:body_mass_g) |> 
  map_dbl(~ mean(.x, na.rm = TRUE))
   bill_length_mm     bill_depth_mm flipper_length_mm       body_mass_g 
         43.92193          17.15117         200.91520        4201.75439

Output is a vector of doubles.

Calculate the number of NAs in each column.

penguins |> 
  map_int(~ sum(is.na(.x)))
          species            island    bill_length_mm     bill_depth_mm 
                0                 0                 2                 2 
flipper_length_mm       body_mass_g               sex              year 
                2                 2                11                 0

Output is a vector of integers.

Calculate the number of NAs in each column.

penguins |> 
  map_lgl(~ sum(is.na(.x)))
Error in `map_lgl()`:
ℹ In index: 3.
ℹ With name: bill_length_mm.
Caused by error:
! Can't coerce from an integer to a logical.

R returns an error if the output is of the wrong type!

  • Output is a logical vector??

Comparing Speed

Using functional programming can be much faster than using for loops.

loop_func <- function(d){
  typ <- rep(NA, ncol(d))
  for(i in 1:ncol(d)){
    typ[i] <- class(d[,i])
  }
  return(typ)
}
map_func <- function(d){
  typ <- map_chr(d, class)
  return(typ)
}
df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 7))

loop_func(df)
[1] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
map_func(df)
       V1        V2        V3        V4        V5        V6        V7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 100000))

microbenchmark::microbenchmark(loop_func(df),
                               map_func(df),
                               times = 20)

map_if()

The map_if() function allows us to conditionally apply a function to each item in a list.

penguins |> 
  mutate(across(.cols = where(is.numeric), 
                .fns = scale))
# A tibble: 8 × 5
  species island    bill_length_mm[,1] bill_depth_mm[,1] sex   
  <fct>   <fct>                  <dbl>             <dbl> <fct> 
1 Adelie  Torgersen             -0.883             0.784 male  
2 Adelie  Torgersen             -0.810             0.126 female
3 Adelie  Torgersen             -0.663             0.430 female
4 Adelie  Torgersen             NA                NA     <NA>  
5 Adelie  Torgersen             -1.32              1.09  female
6 Adelie  Torgersen             -0.847             1.75  male  
7 Adelie  Torgersen             -0.920             0.329 female
8 Adelie  Torgersen             -0.865             1.24  male  
penguins |> 
  map_if(is.numeric, scale)
$species
  [1] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
  [8] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [15] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [22] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [29] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [36] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [43] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [50] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [57] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [64] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [71] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [78] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [85] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [92] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
 [99] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[106] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[113] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[120] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[127] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[134] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[141] Adelie    Adelie    Adelie    Adelie    Adelie    Adelie    Adelie   
[148] Adelie    Adelie    Adelie    Adelie    Adelie    Gentoo    Gentoo   
[155] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[162] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[169] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[176] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[183] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[190] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[197] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[204] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[211] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[218] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[225] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[232] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[239] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[246] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[253] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[260] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[267] Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo    Gentoo   
[274] Gentoo    Gentoo    Gentoo    Chinstrap Chinstrap Chinstrap Chinstrap
[281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
[344] Chinstrap
Levels: Adelie Chinstrap Gentoo

$island
  [1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
  [8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe   
 [22] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [29] Biscoe    Biscoe    Dream     Dream     Dream     Dream     Dream    
 [36] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [43] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [50] Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [57] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
 [64] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen
 [71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
 [85] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [92] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
 [99] Dream     Dream     Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[106] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[113] Biscoe    Biscoe    Biscoe    Biscoe    Torgersen Torgersen Torgersen
[120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
[127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream    
[134] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[141] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[148] Dream     Dream     Dream     Dream     Dream     Biscoe    Biscoe   
[155] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[162] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[169] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[176] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[183] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[190] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[197] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[204] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[211] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[218] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[225] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[232] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[239] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[246] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[253] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[260] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[267] Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe    Biscoe   
[274] Biscoe    Biscoe    Biscoe    Dream     Dream     Dream     Dream    
[281] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[288] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[295] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[302] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[309] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[316] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[323] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[330] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[337] Dream     Dream     Dream     Dream     Dream     Dream     Dream    
[344] Dream    
Levels: Biscoe Dream Torgersen

$bill_length_mm
              [,1]
  [1,] -0.88320467
  [2,] -0.80993901
  [3,] -0.66340769
  [4,]          NA
  [5,] -1.32279862
  [6,] -0.84657184
  [7,] -0.91983750
  [8,] -0.86488825
  [9,] -1.79902541
 [10,] -0.35202864
 [11,] -1.12131806
 [12,] -1.12131806
 [13,] -0.51687637
 [14,] -0.97478674
 [15,] -1.70744334
 [16,] -1.34111504
 [17,] -0.95647033
 [18,] -0.26044656
 [19,] -1.74407616
 [20,]  0.38062795
 [21,] -1.12131806
 [22,] -1.13963448
 [23,] -1.46932994
 [24,] -1.04805240
 [25,] -0.93815391
 [26,] -1.57922843
 [27,] -0.60845845
 [28,] -0.62677486
 [29,] -1.10300165
 [30,] -0.62677486
 [31,] -0.80993901
 [32,] -1.23121655
 [33,] -0.80993901
 [34,] -0.55350920
 [35,] -1.37774787
 [36,] -0.86488825
 [37,] -0.93815391
 [38,] -0.31539581
 [39,] -1.15795089
 [40,] -0.75498976
 [41,] -1.35943145
 [42,] -0.57182562
 [43,] -1.45101353
 [44,]  0.03261607
 [45,] -1.26784938
 [46,] -0.79162259
 [47,] -0.51687637
 [48,] -1.17626731
 [49,] -1.45101353
 [50,] -0.29707939
 [51,] -0.79162259
 [52,] -0.70004052
 [53,] -1.63417768
 [54,] -0.35202864
 [55,] -1.72575975
 [56,] -0.46192713
 [57,] -0.90152108
 [58,] -0.60845845
 [59,] -1.35943145
 [60,] -1.15795089
 [61,] -1.50596277
 [62,] -0.48024354
 [63,] -1.15795089
 [64,] -0.51687637
 [65,] -1.37774787
 [66,] -0.42529430
 [67,] -1.54259560
 [68,] -0.51687637
 [69,] -1.46932994
 [70,] -0.38866147
 [71,] -1.90892390
 [72,] -0.77330618
 [73,] -0.79162259
 [74,]  0.34399512
 [75,] -1.54259560
 [76,] -0.20549732
 [77,] -0.55350920
 [78,] -1.23121655
 [79,] -1.41438070
 [80,] -0.33371222
 [81,] -1.70744334
 [82,] -0.18718091
 [83,] -1.32279862
 [84,] -1.61586126
 [85,] -1.21290014
 [86,] -0.48024354
 [87,] -1.39606428
 [88,] -1.28616579
 [89,] -1.02973599
 [90,] -0.91983750
 [91,] -1.50596277
 [92,] -0.51687637
 [93,] -1.81734182
 [94,] -0.79162259
 [95,] -1.41438070
 [96,] -0.57182562
 [97,] -1.06636882
 [98,] -0.66340769
 [99,] -1.98218956
[100,] -0.13223166
[101,] -1.63417768
[102,] -0.53519279
[103,] -1.13963448
[104,] -1.12131806
[105,] -1.10300165
[106,] -0.77330618
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[108,] -1.04805240
[109,] -1.06636882
[110,] -0.13223166
[111,] -1.06636882
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[113,] -0.77330618
[114,] -0.31539581
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[117,] -0.97478674
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[120,] -0.51687637
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[122,] -1.13963448
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[165,]  0.28904588
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[167,]  0.34399512
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[170,]  0.96675323
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[172,]  0.87517115
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[175,]  0.47221003
[176,]  0.43557720
[177,] -0.18718091
[178,]  0.39894437
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[180,]  0.71032342
[181,]  0.78358908
[182,]  1.11328455
[183,]  0.61874135
[184,] -0.20549732
[185,]  0.21578022
[186,]  2.87166037
[187,]  0.94843681
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[189,] -0.24213015
[190,]  0.08756532
[191,]  0.01429966
[192,]  0.87517115
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[194,]  1.04001889
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[201,]  0.17914739
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[204,]  0.83853832
[205,]  0.21578022
[206,]  1.13160096
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[209,] -0.02233317
[210,]  0.28904588
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[212,]  1.18655021
[213,]  0.25241305
[214,]  0.41726078
[215,]  0.32567871
[216,]  1.90089038
[217,]  0.34399512
[218,]  1.07665172
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[220,]  1.02170247
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[224,]  0.45389361
[225,]  0.78358908
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[228,]  0.85685474
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[230,]  1.31476511
[231,]  0.23409663
[232,]  0.23409663
[233,]  0.94843681
[234,]  1.57119492
[235,]  0.63705776
[236,]  1.11328455
[237,]  0.17914739
[238,]  1.25981586
[239,] -0.09559883
[240,]  1.35139794
[241,]  0.65537418
[242,]  1.49792926
[243,]  0.65537418
[244,]  1.51624567
[245,]  0.28904588
[246,]  1.02170247
[247,]  0.10588173
[248,]  1.25981586
[249,]  1.00338606
[250,]  0.54547569
[251,]  0.82022191
[252,]  1.31476511
[253,]  0.83853832
[254,]  2.19395302
[255,]  0.60042493
[256,]  0.94843681
[257,]  0.61874135
[258,]  0.52715927
[259,] -0.40697788
[260,]  1.73604265
[261,] -0.11391525
[262,]  0.76527266
[263,]  1.20486662
[264,]  1.07665172
[265,] -0.07728242
[266,]  1.38803077
[267,]  0.41726078
[268,]  2.04742170
[269,]  0.10588173
[270,]  0.89348757
[271,]  0.60042493
[272,]          NA
[273,]  0.52715927
[274,]  1.18655021
[275,]  0.23409663
[276,]  1.09496813
[277,]  0.47221003
[278,]  1.11328455
[279,]  1.35139794
[280,]  0.27072946
[281,]  1.60782775
[282,]  0.23409663
[283,]  0.39894437
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[293,]  1.16823379
[294,]  2.57859773
[295,]  0.45389361
[296,]  0.96675323
[297,] -0.27876298
[298,]  0.83853832
[299,] -0.13223166
[300,]  1.22318303
[301,]  0.50884286
[302,]  1.47961284
[303,]  1.20486662
[304,]  1.02170247
[305,]  0.45389361
[306,]  1.62614416
[307,] -0.55350920
[308,]  1.88257397
[309,] -0.26044656
[310,]  1.29644869
[311,]  1.05833530
[312,]  0.65537418
[313,]  0.67369059
[314,]  1.47961284
[315,]  0.54547569
[316,]  1.75435906
[317,]  0.93012040
[318,]  0.41726078
[319,]  1.27813228
[320,]  0.28904588
[321,]  1.27813228
[322,]  1.25981586
[323,]  1.13160096
[324,]  0.93012040
[325,]  1.38803077
[326,]  1.07665172
[327,]  0.76527266
[328,]  1.36971435
[329,]  0.32567871
[330,]  1.24149945
[331,] -0.26044656
[332,]  1.51624567
[333,]  0.23409663
[334,]  0.98506964
[335,]  1.14991738
[336,]  0.30736229
[337,]  1.46129643
[338,]  0.52715927
[339,]  0.32567871
[340,]  2.17563660
[341,] -0.07728242
[342,]  1.04001889
[343,]  1.25981586
[344,]  1.14991738
attr(,"scaled:center")
[1] 43.92193
attr(,"scaled:scale")
[1] 5.459584

$bill_depth_mm
              [,1]
  [1,]  0.78430007
  [2,]  0.12600328
  [3,]  0.42983257
  [4,]          NA
  [5,]  1.08812936
  [6,]  1.74642615
  [7,]  0.32855614
  [8,]  1.24004400
  [9,]  0.48047078
 [10,]  1.54387329
 [11,] -0.02591137
 [12,]  0.07536506
 [13,]  0.22727971
 [14,]  2.05025544
 [15,]  1.99961722
 [16,]  0.32855614
 [17,]  0.93621471
 [18,]  1.79706436
 [19,]  0.63238542
 [20,]  2.20217008
 [21,]  0.58174721
 [22,]  0.78430007
 [23,]  1.03749114
 [24,]  0.48047078
 [25,]  0.02472685
 [26,]  0.88557650
 [27,]  0.73366185
 [28,]  0.37919435
 [29,]  0.73366185
 [30,]  0.88557650
 [31,] -0.22846423
 [32,]  0.48047078
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 [35,] -0.07654958
 [36,]  1.99961722
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 [38,]  0.68302364
 [39,]  1.08812936
 [40,]  0.98685293
 [41,]  0.42983257
 [42,]  0.63238542
 [43,]  0.68302364
 [44,]  1.29068222
 [45,] -0.12718780
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 [47,]  0.93621471
 [48,]  0.88557650
 [49,]  0.37919435
 [50,]  2.05025544
 [51,]  0.27791792
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 [54,]  1.18940579
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 [58,]  0.83493828
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 [70,]  1.13876757
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 [77,] -0.17782601
 [78,]  1.13876757
 [79,] -0.53229351
 [80,]  0.98685293
 [81,]  0.02472685
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 [83,]  0.83493828
 [84,]  1.13876757
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 [90,]  0.83493828
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[100,]  0.68302364
[101,]  0.37919435
[102,]  1.44259686
[103,] -0.58293173
[104,]  1.44259686
[105,]  0.73366185
[106,]  0.88557650
[107,]  0.02472685
[108,]  1.44259686
[109,] -0.07654958
[110,]  0.93621471
[111,] -0.32974066
[112,]  1.59451151
[113,]  0.27791792
[114,]  1.18940579
[115,]  1.79706436
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[117,] -0.07654958
[118,]  1.69578793
[119,] -0.07654958
[120,]  0.73366185
[121,]  0.02472685
[122,]  1.34132043
[123,] -0.07654958
[124,]  0.68302364
[125,] -0.63356994
[126,]  0.93621471
[127,]  0.22727971
[128,]  0.58174721
[129,] -0.02591137
[130,]  0.42983257
[131,]  0.37919435
[132,]  1.03749114
[133,]  0.68302364
[134,]  0.68302364
[135,]  0.22727971
[136,]  0.17664149
[137,]  0.17664149
[138,]  1.49323508
[139,] -0.32974066
[140,]  0.37919435
[141,] -0.02591137
[142,]  0.02472685
[143,] -0.83612280
[144,] -0.07654958
[145,] -0.17782601
[146,]  0.78430007
[147,]  0.73366185
[148,]  0.63238542
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[150,]  0.48047078
[151,] -0.02591137
[152,]  0.68302364
[153,] -2.00080174
[154,] -0.43101709
[155,] -1.54505781
[156,] -0.98803745
[157,] -1.34250495
[158,] -1.84888710
[159,] -1.29186674
[160,] -0.93739923
[161,] -1.89952531
[162,] -0.88676102
[163,] -1.74761067
[164,] -0.53229351
[165,] -1.74761067
[166,] -1.29186674
[167,] -1.29186674
[168,] -0.73484637
[169,] -1.84888710
[170,] -0.98803745
[171,] -1.34250495
[172,] -1.03867566
[173,] -1.44378138
[174,] -1.34250495
[175,] -1.34250495
[176,] -0.68420816
[177,] -2.05143996
[178,] -1.03867566
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[180,] -1.08931388
[181,] -1.44378138
[182,] -0.93739923
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[184,] -1.49441960
[185,] -1.34250495
[186,] -0.07654958
[187,] -1.19059031
[188,] -0.43101709
[189,] -1.74761067
[190,]  0.07536506
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[192,] -0.73484637
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[194,] -0.58293173
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[196,] -1.08931388
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[198,] -1.64633424
[199,] -1.64633424
[200,] -0.63356994
[201,] -1.95016353
[202,] -0.68420816
[203,] -1.49441960
[204,] -1.54505781
[205,] -1.39314317
[206,] -1.08931388
[207,] -1.39314317
[208,] -0.88676102
[209,] -1.64633424
[210,] -1.08931388
[211,] -1.34250495
[212,] -0.93739923
[213,] -1.69697245
[214,] -1.13995209
[215,] -1.64633424
[216,] -0.73484637
[217,] -1.49441960
[218,] -0.17782601
[219,] -1.39314317
[220,] -0.48165530
[221,] -1.49441960
[222,] -1.08931388
[223,] -1.08931388
[224,] -0.78548459
[225,] -0.78548459
[226,] -1.19059031
[227,] -1.08931388
[228,] -0.58293173
[229,] -1.49441960
[230,] -0.43101709
[231,] -1.69697245
[232,] -0.38037887
[233,] -1.34250495
[234,] -0.78548459
[235,] -1.29186674
[236,] -0.63356994
[237,] -1.69697245
[238,]  0.07536506
[239,] -1.39314317
[240,] -1.49441960
[241,] -1.59569603
[242,] -0.07654958
[243,] -1.08931388
[244,] -0.02591137
[245,] -1.34250495
[246,] -0.53229351
[247,] -1.24122852
[248,] -0.73484637
[249,] -0.68420816
[250,] -1.29186674
[251,] -1.39314317
[252,] -0.32974066
[253,] -1.08931388
[254,] -0.07654958
[255,] -0.83612280
[256,] -1.08931388
[257,] -1.69697245
[258,] -0.53229351
[259,] -1.24122852
[260,] -0.68420816
[261,] -1.59569603
[262,] -1.03867566
[263,] -0.98803745
[264,] -0.63356994
[265,] -0.98803745
[266,] -0.43101709
[267,] -1.54505781
[268,] -0.58293173
[269,] -0.73484637
[270,] -0.48165530
[271,] -1.74761067
[272,]          NA
[273,] -1.44378138
[274,] -0.73484637
[275,] -1.19059031
[276,] -0.53229351
[277,]  0.37919435
[278,]  1.18940579
[279,]  1.03749114
[280,]  0.78430007
[281,]  1.34132043
[282,]  0.32855614
[283,]  0.53110900
[284,]  0.53110900
[285,]  0.88557650
[286,]  1.39195865
[287,]  0.32855614
[288,]  1.59451151
[289,]  0.07536506
[290,]  0.48047078
[291,] -0.02591137
[292,]  1.24004400
[293,]  1.44259686
[294,]  0.32855614
[295,]  0.73366185
[296,]  0.53110900
[297,]  0.07536506
[298,]  0.17664149
[299,] -0.27910244
[300,]  1.13876757
[301,]  0.37919435
[302,]  0.93621471
[303,]  0.63238542
[304,]  0.93621471
[305,]  0.32855614
[306,]  1.44259686
[307,] -0.27910244
[308,]  1.84770258
[309,] -0.22846423
[310,]  0.83493828
[311,]  0.73366185
[312,] -0.17782601
[313,]  0.58174721
[314,]  1.79706436
[315,] -0.27910244
[316,]  1.39195865
[317,]  1.18940579
[318,]  0.17664149
[319,]  0.98685293
[320,] -0.07654958
[321,]  0.37919435
[322,]  0.68302364
[323,]  0.37919435
[324,]  1.24004400
[325,]  0.78430007
[326,]  0.07536506
[327,] -0.38037887
[328,]  0.93621471
[329,]  0.07536506
[330,]  1.29068222
[331,]  0.07536506
[332,]  0.83493828
[333,] -0.27910244
[334,]  1.39195865
[335,]  0.83493828
[336,]  1.13876757
[337,]  1.18940579
[338,] -0.32974066
[339,] -0.07654958
[340,]  1.34132043
[341,]  0.48047078
[342,]  0.53110900
[343,]  0.93621471
[344,]  0.78430007
attr(,"scaled:center")
[1] 17.15117
attr(,"scaled:scale")
[1] 1.974793

$flipper_length_mm
               [,1]
  [1,] -1.416271525
  [2,] -1.060696087
  [3,] -0.420660299
  [4,]           NA
  [5,] -0.562890474
  [6,] -0.776235737
  [7,] -1.416271525
  [8,] -0.420660299
  [9,] -0.562890474
 [10,] -0.776235737
 [11,] -1.060696087
 [12,] -1.487386613
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 [15,] -0.207315036
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 [20,] -0.491775386
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 [24,] -1.131811175
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 [30,] -1.487386613
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 [41,] -1.345156438
 [42,] -0.420660299
 [43,] -1.060696087
 [44,] -0.349545211
 [45,] -1.131811175
 [46,] -0.776235737
 [47,] -1.345156438
 [48,] -1.558501700
 [49,] -0.776235737
 [50,] -0.705120649
 [51,] -1.060696087
 [52,] -0.918465912
 [53,] -0.776235737
 [54,] -0.065084861
 [55,] -0.989581000
 [56,] -0.705120649
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 [60,] -0.491775386
 [61,] -1.131811175
 [62,] -0.420660299
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attr(,"scaled:scale")
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$body_mass_g
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attr(,"scaled:center")
[1] 4201.754
attr(,"scaled:scale")
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$sex
  [1] male   female female <NA>   female male   female male   <NA>   <NA>  
 [11] <NA>   <NA>   female male   male   female female male   female male  
 [21] female male   female male   male   female male   female female male  
 [31] female male   female male   female male   male   female female male  
 [41] female male   female male   female male   male   <NA>   female male  
 [51] female male   female male   female male   female male   female male  
 [61] female male   female male   female male   female male   female male  
 [71] female male   female male   female male   female male   female male  
 [81] female male   female male   female male   male   female male   female
 [91] female male   female male   female male   female male   female male  
[101] female male   female male   female male   female male   female male  
[111] female male   female male   female male   female male   female male  
[121] female male   female male   female male   female male   female male  
[131] female male   female male   female male   female male   female male  
[141] female male   female male   female male   male   female female male  
[151] female male   female male   female male   male   female female male  
[161] female male   female male   female male   female male   female male  
[171] female male   male   female female male   female male   <NA>   male  
[181] female male   male   female female male   female male   female male  
[191] female male   female male   female male   male   female female male  
[201] female male   female male   female male   female male   female male  
[211] female male   female male   female male   female male   <NA>   male  
[221] female male   female male   male   female female male   female male  
[231] female male   female male   female male   female male   female male  
[241] female male   female male   female male   female male   male   female
[251] female male   female male   female male   <NA>   male   female male  
[261] female male   female male   female male   female male   <NA>   male  
[271] female <NA>   female male   female male   female male   male   female
[281] male   female female male   female male   female male   female male  
[291] female male   male   female female male   female male   female male  
[301] female male   female male   female male   female male   female male  
[311] male   female female male   female male   male   female male   female
[321] female male   female male   male   female female male   female male  
[331] female male   female male   male   female male   female female male  
[341] female male   male   female
Levels: female male

$year
              [,1]
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attr(,"scaled:center")
[1] 2008.029
attr(,"scaled:scale")
[1] 0.8183559
penguins |> 
  map_if(is.numeric, scale) |> 
  as.tibble()
# A tibble: 8 × 5
  species island    bill_length_mm[,1] bill_depth_mm[,1] sex   
  <fct>   <fct>                  <dbl>             <dbl> <fct> 
1 Adelie  Torgersen             -0.883             0.784 male  
2 Adelie  Torgersen             -0.810             0.126 female
3 Adelie  Torgersen             -0.663             0.430 female
4 Adelie  Torgersen             NA                NA     <NA>  
5 Adelie  Torgersen             -1.32              1.09  female
6 Adelie  Torgersen             -0.847             1.75  male  
7 Adelie  Torgersen             -0.920             0.329 female
8 Adelie  Torgersen             -0.865             1.24  male

The pmap() Family

These functions take in a list of vectors and a function.

  • The function must accept a number of arguments equal to the length of the list,

The pmap() Family

The vectors need to have the same names as the arguments of the function you are applying.

fruit <- data.frame(string = c("apple", "banana", "cherry"),
                    pattern = c("p", "n", "h"),
                    replacement = c("P", "N", "H"))
fruit
  string pattern replacement
1  apple       p           P
2 banana       n           N
3 cherry       h           H
fruit |> 
  pmap_chr(str_replace_all)
[1] "aPPle"  "baNaNa" "cHerry"

The map() and pmap() Family


There are so many functions – check out the purrr cheatsheet!

Use functional programming!

https://bookdown.org/hneth/ds4psy

PA 8: The Twelve Days of Christmas

https://studioplayhouse.org/the-12-days-of-christmas/

glue()

The glue package embeds R expressions in curly brackets that are then evaluated and inserted into the argument string.

library(glue)

name <- "Dr. Rehnberg"
glue('My name is {name}.')
My name is Dr. Rehnberg.


This will be a handy function (and package) for putting our song together!

An Example

99 bottles of beer on the wall, 99 bottles of beer. Take one down, pass it around, 98 bottles of beer on the wall…

bottles_lyrics <- function(n){
  lyrics <- glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  return(lyrics)
}

bottles_lyrics(3)
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
bottles_song <- function(n){
  song <- map_chr(n:0, bottles_lyrics)
  return(glue("{song}"))
}

bottles_song(3)
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
0 bottles of beer on the wall, 0 bottles of beer 
Take one down, pass it around, -1 bottles of beer on the wall

No more bottles of beer on the wall, no more bottles of beer. Go to the store, buy some more, 99 bottles of beer on the wall…

bottles_lyrics <- function(n){
  if(n == 0){
    lyrics <- glue("No more bottles of beer on the wall, no more bottles of beer. \nGo to the store, buy some more, 99 bottles of beer on the wall...")
  } else{
    lyrics <- glue("{n} bottles of beer on the wall, {n} bottles of beer \nTake one down, pass it around, {n -1} bottles of beer on the wall")
  }
  return(lyrics)
}
bottles_song(4)
4 bottles of beer on the wall, 4 bottles of beer 
Take one down, pass it around, 3 bottles of beer on the wall
3 bottles of beer on the wall, 3 bottles of beer 
Take one down, pass it around, 2 bottles of beer on the wall
2 bottles of beer on the wall, 2 bottles of beer 
Take one down, pass it around, 1 bottles of beer on the wall
1 bottles of beer on the wall, 1 bottles of beer 
Take one down, pass it around, 0 bottles of beer on the wall
No more bottles of beer on the wall, no more bottles of beer. 
Go to the store, buy some more, 99 bottles of beer on the wall...

To do…

  • PA 8: The Twelve Days of Christmas Starter Functions
    • Due Wednesday 5/22 at 10:00am.
  • Project Proposal + Data
    • Due Tuesday 5/28 at 11:59pm.


Tuesday, 5/28 follows a Monday schedule!

Wednesday, May 22

Today we will…

  • Midterm Exam (extra point on SA and OEA)
  • New Material
    • The map2() Family
  • Work Time
    • Lab 8: The Twelve Days of Christmas
    • PC3: Project Proposal + Data

Comparing Speed

Using for loops can be much slower than using functional programming.

loop_func <- function(d){
  typ <- rep(NA, ncol(d))
  for(i in 1:ncol(d)){
    typ[i] <- class(d[,i])
  }
  return(typ)
}
map_func <- function(d){
  typ <- map_chr(d, class)
  return(typ)
}
df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 7))

loop_func(df)
[1] "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
map_func(df)
       V1        V2        V3        V4        V5        V6        V7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
df <- as.data.frame(matrix(1,
                           nrow = 5,
                           ncol = 100000))

microbenchmark::microbenchmark(loop_func(df),
                               map_func(df),
                               times = 20)

The map2() Family

These functions allow us to iterate over two lists at the same time.

The map2() Family

These functions include:

  • map2()
  • map2_chr()
  • map2_lgl()
  • map2_int()
  • map2_dbl()

Each function has two list arguments, denoted .x and .y, and a function argument.

Small map2() Example

Find the minimum.

a <- c(1, 2, 4)
b <- c(6, 5, 3)

map2_chr(a, b,
         ~ str_glue("The minimum of {.x} and {.y} is {min(.x, .y)}."))
[1] "The minimum of 1 and 6 is 1." "The minimum of 2 and 5 is 2."
[3] "The minimum of 4 and 3 is 3."

Big map2() Example

  • Regress vehicle MPG on vehicle weight.
  • Do the regression separately for 4-, 6-, and 8-cylinder vehicles.
(by_cyl <- mtcars |>
   split(mtcars$cyl))
$`4`
                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

$`6`
                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6

$`8`
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

Fit a linear regression model to each subset of the data.

  • Regress vehicle MPG on vehicle weight.
  • We are fitting three separate models.
mods <- by_cyl |>
  map(~ lm(mpg ~ wt, data = .x))

Predict vehicle MPG from observed vehicle weight.

  • The predict() function needs two inputs.
map2(mods, by_cyl, predict)
$`4`
    Datsun 710      Merc 240D       Merc 230       Fiat 128    Honda Civic 
      26.47010       21.55719       21.78307       27.14774       30.45125 
Toyota Corolla  Toyota Corona      Fiat X1-9  Porsche 914-2   Lotus Europa 
      29.20890       25.65128       28.64420       27.48656       31.02725 
    Volvo 142E 
      23.87247 

$`6`
     Mazda RX4  Mazda RX4 Wag Hornet 4 Drive        Valiant       Merc 280 
      21.12497       20.41604       19.47080       18.78968       18.84528 
     Merc 280C   Ferrari Dino 
      18.84528       20.70795 

$`8`
  Hornet Sportabout          Duster 360          Merc 450SE          Merc 450SL 
           16.32604            16.04103            14.94481            15.69024 
        Merc 450SLC  Cadillac Fleetwood Lincoln Continental   Chrysler Imperial 
           15.58061            12.35773            11.97625            12.14945 
   Dodge Challenger         AMC Javelin          Camaro Z28    Pontiac Firebird 
           16.15065            16.33700            15.44907            15.43811 
     Ford Pantera L       Maserati Bora 
           16.91800            16.04103

nest() and unnest()

  • We can pair functions from the map() family very nicely with two tidyr functions: nest() and unnest().
  • These allow us to easily map functions onto subsets of the data.

nest()

Nest subsets of the data (as tibbles) inside a tibble.

  • Specify the column(s) to create subsets on.
mtcars |> 
  nest(cyl_data = -cyl)
# A tibble: 3 × 2
    cyl cyl_data          
  <dbl> <list>            
1     6 <tibble [7 × 10]> 
2     4 <tibble [11 × 10]>
3     8 <tibble [14 × 10]>
mtcars |> 
  nest(cg_data = -c(cyl,gear))
# A tibble: 8 × 3
    cyl  gear cg_data          
  <dbl> <dbl> <list>           
1     6     4 <tibble [4 × 9]> 
2     4     4 <tibble [8 × 9]> 
3     6     3 <tibble [2 × 9]> 
4     8     3 <tibble [12 × 9]>
5     4     3 <tibble [1 × 9]> 
6     4     5 <tibble [2 × 9]> 
7     8     5 <tibble [2 × 9]> 
8     6     5 <tibble [1 × 9]>

unnest()

Un-nest the data by row binding the subsets back together.

  • Specify the column(s) that contains the subsets.
mtcars |> 
  nest(cyl_data = -cyl) |> 
  unnest(cyl_data) |> 
  head()
# A tibble: 6 × 11
    cyl   mpg  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     6  21    160    110  3.9   2.62  16.5     0     1     4     4
2     6  21    160    110  3.9   2.88  17.0     0     1     4     4
3     6  21.4  258    110  3.08  3.22  19.4     1     0     3     1
4     6  18.1  225    105  2.76  3.46  20.2     1     0     3     1
5     6  19.2  168.   123  3.92  3.44  18.3     1     0     4     4
6     6  17.8  168.   123  3.92  3.44  18.9     1     0     4     4

Big map2() Example (Again)

mtcars |>
  nest(cyl_data = -cyl)
# A tibble: 3 × 2
    cyl cyl_data          
  <dbl> <list>            
1     6 <tibble [7 × 10]> 
2     4 <tibble [11 × 10]>
3     8 <tibble [14 × 10]>
mtcars |>
  nest(cyl_data = -cyl) |>
  mutate(mod = map(cyl_data, 
                   ~ lm(mpg ~ wt, data = .x)))
# A tibble: 3 × 3
    cyl cyl_data           mod   
  <dbl> <list>             <list>
1     6 <tibble [7 × 10]>  <lm>  
2     4 <tibble [11 × 10]> <lm>  
3     8 <tibble [14 × 10]> <lm>  
mtcars |>
  nest(cyl_data = -cyl) |>
  mutate(mod = map(cyl_data, 
                   ~ lm(mpg ~ wt, data = .x)),
         pred_mpg = map2(.x = mod,
                         .y = cyl_data, 
                         .f = ~ predict(object = .x, data = .y)))
# A tibble: 3 × 4
    cyl cyl_data           mod    pred_mpg  
  <dbl> <list>             <list> <list>    
1     6 <tibble [7 × 10]>  <lm>   <dbl [7]> 
2     4 <tibble [11 × 10]> <lm>   <dbl [11]>
3     8 <tibble [14 × 10]> <lm>   <dbl [14]>
mtcars |>
  nest(cyl_data = -cyl) |>
  mutate(mod = map(cyl_data, 
                   ~ lm(mpg ~ wt, data = .x)),
         pred_mpg = map2(.x = mod,
                         .y = cyl_data,
                         .f = ~ predict(object = .x, data = .y))) |> 
  select(-mod) |> 
  unnest(cols = c(cyl_data, pred_mpg)) |> 
  select(cyl, wt, mpg, pred_mpg)
# A tibble: 32 × 4
     cyl    wt   mpg pred_mpg
   <dbl> <dbl> <dbl>    <dbl>
 1     6  2.62  21       21.1
 2     6  2.88  21       20.4
 3     6  3.22  21.4     19.5
 4     6  3.46  18.1     18.8
 5     6  3.44  19.2     18.8
 6     6  3.44  17.8     18.8
 7     6  2.77  19.7     20.7
 8     4  2.32  22.8     26.5
 9     4  3.19  24.4     21.6
10     4  3.15  22.8     21.8
# ℹ 22 more rows

Lab 8: The Twelve Days of Christmas

  1. Write a sing_day() function.

Tip

  • Start with a small example and test it on a single day outside of the function.
  • str_flatten() might be useful – find its arguments in the documentation.
  1. Iterate your sing_day() function over all days.
  2. Style your output!

To do…

  • Lab 8: The Twelve Days of Christmas
    • Due Saturday, 5/25 at 11:59pm.
  • Project Proposal + Data
    • Due Tuesday, 5/28 at 11:59pm.
  • Read Chapter 9: Regression & Simulation
    • Check-in 9.1 & 9.2 due Tuesday, 5/28 at 10:00am.